Self-Evaluation of Large Language Model based on Glass-box Features

Hui Huang, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao, Wenpeng Lu


Abstract
The proliferation of open-source Large Language Models (LLMs) underscores the pressing need for evaluation methods. Existing works primarily rely on external evaluators, focusing on training and prompting strategies. However, a crucial aspect – model-aware glass-box features – is overlooked. In this study, we explore the utility of glass-box features under the scenario of self-evaluation, namely applying an LLM to evaluate its own output. We investigate various glass-box feature groups and discovered that the softmax distribution serves as a reliable quality indicator for self-evaluation. Experimental results on public benchmarks validate the feasibility of self-evaluation of LLMs using glass-box features.
Anthology ID:
2024.findings-emnlp.333
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5813–5820
Language:
URL:
https://aclanthology.org/2024.findings-emnlp.333/
DOI:
10.18653/v1/2024.findings-emnlp.333
Bibkey:
Cite (ACL):
Hui Huang, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao, and Wenpeng Lu. 2024. Self-Evaluation of Large Language Model based on Glass-box Features. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 5813–5820, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Self-Evaluation of Large Language Model based on Glass-box Features (Huang et al., Findings 2024)
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PDF:
https://aclanthology.org/2024.findings-emnlp.333.pdf